Papers Molecular Graph Generation
“Molecular Graph Generation” 태그가 달린 논문 54편 · 필터 해제
Pretraining Generative Flow Networks with Inexpensive Rewards for Molecular Graph Generation
Generative Flow Networks (GFlowNets) have recently emerged as a suitable framework for generating diverse and high-quality molecular structures by learning from rewards treated as unnormalized distributions. Previous wor…
Drug DesignGraph GenerationMolecular Graph GenerationUnsupervised Pre-trainingLearning-Order Autoregressive Models with Application to Molecular Graph Generation
Autoregressive models (ARMs) have become the workhorse for sequence generation tasks, since many problems can be modeled as next-token prediction. While there appears to be a natural ordering for text (i.e., left-to-righ…
Graph GenerationMolecular Graph GenerationFragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching
We introduce FragFM, a novel hierarchical framework via fragment-level discrete flow matching for efficient molecular graph generation. FragFM generates molecules at the fragment level, leveraging a coarse-to-fine autoen…
DiversityDrug DiscoveryEfficient ExplorationGraph Generation+1Flatten Graphs as Sequences: Transformers are Scalable Graph Generators
We introduce AutoGraph, a novel autoregressive framework for generating large attributed graphs using decoder-only transformers. At the core of our approach is a reversible "flattening" process that transforms graphs int…
DecoderGraph GenerationLanguage ModelingLanguage Modelling+1Improving Molecular Graph Generation with Flow Matching and Optimal Transport
Generating molecular graphs is crucial in drug design and discovery but remains challenging due to the complex interdependencies between nodes and edges. While diffusion models have demonstrated their potentiality in mol…
Drug DesignGraph GenerationMolecular Graph GenerationGUISE: Graph GaUssIan Shading watErmark
In the expanding field of generative artificial intelligence, integrating robust watermarking technologies is essential to protect intellectual property and maintain content authenticity. Traditionally, watermarking tech…
Graph GenerationMolecular Graph GenerationTraining-Free Guidance for Discrete Diffusion Models for Molecular Generation
Training-free guidance methods for continuous data have seen an explosion of interest due to the fact that they enable foundation diffusion models to be paired with interchangable guidance models. Currently, equivalent g…
Graph GenerationMolecular Graph GenerationInstruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion Model
Recent advancements in computational chemistry have increasingly focused on synthesizing molecules based on textual instructions. Integrating graph generation with these instructions is complex, leading most current meth…
Computational chemistryDenoisingGraph GenerationMolecular Graph GenerationGraphSPNs: Sum-Product Networks Benefit From Canonical Orderings
Deep generative models have recently made a remarkable progress in capturing complex probability distributions over graphs. However, they are intractable and thus unable to answer even the most basic probabilistic infere…
Molecular Graph GenerationvalidLift Your Molecules: Molecular Graph Generation in Latent Euclidean Space
We introduce a new framework for molecular graph generation with 3D molecular generative models. Our Synthetic Coordinate Embedding (SyCo) framework maps molecular graphs to Euclidean point clouds via synthetic conformer…
Edge ClassificationGraph GenerationGraph Neural NetworkMolecular Graph Generation+13M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation
Generating molecular structures with desired properties is a critical task with broad applications in drug discovery and materials design. We propose 3M-Diffusion, a novel multi-modal molecular graph generation method, t…
DecoderDrug DiscoveryGraph GenerationMolecular Graph Generation+1Overcoming Order in Autoregressive Graph Generation
Graph generation is a fundamental problem in various domains, including chemistry and social networks. Recent work has shown that molecular graph generation using recurrent neural networks (RNNs) is advantageous compared…
Graph GenerationMolecular Graph GenerationvalidA Simple and Scalable Representation for Graph Generation
Recently, there has been a surge of interest in employing neural networks for graph generation, a fundamental statistical learning problem with critical applications like molecule design and community analysis. However, …
Graph GenerationMolecular Graph GenerationWill More Expressive Graph Neural Networks do Better on Generative Tasks?
Graph generation poses a significant challenge as it involves predicting a complete graph with multiple nodes and edges based on simply a given label. This task also carries fundamental importance to numerous real-world …
Bayesian OptimisationGraph GenerationGraph Neural NetworkMolecular Graph GenerationLearning Joint 2D & 3D Diffusion Models for Complete Molecule Generation
Designing new molecules is essential for drug discovery and material science. Recently, deep generative models that aim to model molecule distribution have made promising progress in narrowing down the chemical research …
3D Molecule GenerationDrug DiscoveryGraph GenerationMolecular Graph GenerationMolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation
Molecular de novo design is a critical yet challenging task in scientific fields, aiming to design novel molecular structures with desired property profiles. Significant progress has been made by resorting to generative …
Graph GenerationMolecular Graph GenerationRepresentation LearningTarget Specific De Novo Design of Drug Candidate Molecules with Graph Transformer-based Generative Adversarial Networks
Discovering novel drug candidate molecules is one of the most fundamental and critical steps in drug development. Generative deep learning models, which create synthetic data given a probability distribution, offer a hig…
Generative Adversarial NetworkMolecular Graph GenerationGeometry-Complete Diffusion for 3D Molecule Generation and Optimization
Denoising diffusion probabilistic models (DDPMs) have pioneered new state-of-the-art results in disciplines such as computer vision and computational biology for diverse tasks ranging from text-guided image generation to…
3D Molecule GenerationDenoisingGraph GenerationImage Generation+5Graph Generation with Diffusion Mixture
Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures. Although diffusion models have achieved notable success in graph generation …
3D Molecule GenerationGraph GenerationInductive BiasMolecular Graph GenerationConditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation
Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidl…
Drug DiscoveryGraph GenerationGraph SamplingMolecular Graph Generation